Machine learning has been applied in diverse areas of physical sciences ranging from condensed matter to high energy physics to complex systems. In complex systems, neural network-based machine learning techniques have been used in predicting amplitude death [1]}, anticipation of synchronization [2]}, phase transitions in complex networks [3]}, time series prediction [4]}, etc. In particular, forecasting time series data has attracted interest from the scientific fraternity due to its diverse applications in real-world dynamical systems like stock markets and the brain. However, predicting the time series of a dynamical system has many limitations [5]}, [6]}. Since every data point in a time series is a function of the previous time steps, the error in predicting the future time series data points compounds over time. To avoid prediction error, the correlation matrix of the time series is preferred over the direct prediction of the future time series data points. A correlation matrix of a given multivariate time series data set is useful in several practical real-world scenarios [7]}, [8]}. For instance, by considering fMRI or MEG signals from several brain regions as time series data, one can construct the corresponding correlation matrix, which can then be used to extract the adjacency matrix by setting a threshold value [9]}, [10]}, [11]}.
